Testing British Columbia's water quality guidelines as a mixture of four important contaminants, hardness, exposure time, and species effects
Bibliographic record
Abstract
Provincial water quality guidelines are established in order to prevent detrimental effects \nof a single toxicant from affecting the health of resident aquatic life. However, the \nelevation of pollutants in freshwater can occur from many sources simultaneously and \ninteract to form mixtures. In this study, three common freshwater species, Daphnia \nmagna, Hyalella azteca and Oncorhynchus mykiss, were exposed to cadmium, selenium, \nnitrates and sulphates as a mixture at concentrations the same as British Columbia?s \nprovincial water quality guidelines (BC WQG) for the protection of aquatic life with hard \n(250 mg/L as CaCO3) and soft (50 mg/L as CaCO3) water conditions. For all three \norganisms, both acute (48 hour) and chronic (21 day) exposures were used to examine the \nfour contaminants and their mixture at maximum and average BC WQG concentrations. \nIn the short term exposures, the only treatment that was harmful was cadmium, which \nhad a 43% (p = 0.115, n = 3) and 64% (p < 0.0001, n = 5) mortality for D. magna in soft \nand hard water respectively. The toxicity of the four part mixture (including cadmium) \nwas reduced, due to the antagonistic effect of selenium on the toxicity of cadmium. \nDuring a chronic exposure, the mixture was more (to D. magna) or less hazardous (to H. \nazteca and O. mykiss) than single contaminants; leading to the conclusion that pollutants \ncan have a different overall effect when simultaneously exposed for longer periods of \ntime. Overall, the interactions between pollutants in a complex mixture should be \nconsidered when deriving water quality guidelines. To provide appropriate protection of \nthe environment, these complex interactions should be further investigated with \nrepresentative species in the BC ecosystem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".